After passing through all the chunks, DCC forms a single iteration hypothesis as the union of all the hypotheses (line 19).
DCC calculates the score of this iteration hypothesis on all the examples.
We calculate the score as the number of correctly generalised examples (true positives + true negatives).
In future work, we will explore alternative scoring functions, such as those that minimise description length [1]}.
If the score improves on the best score, DCC updates the best hypothesis (line 22).
Line 22 compresses the chunks.
We delay discussion of this function until we have described the main DCC loop but at a high-level this function tries to merge examples to reduce the number of iterations.
After enumerating all the chunks, DCC doubles the chunk size (line 24) and repeats the process.
Once the loop has finished, DCC returns the best hypothesis.
